Master of Engineering: Engineering Data Analytics (Online)

EPD 416 – ENGINEERING APPLICATIONS OF STATISTICS3 credits.Provides knowledge and skills to apply statistics to many types of engineering problems. Focuses on developing statistically-based experimental techniques and tests for measures of validity, application of computer-based statistical tools, and approaches to distillation of data.Requisites: Graduate/professional standing or declared in Capstone Certificate in Artificial Intelligence for Engineering Data Analytics

ISYE 412 – FUNDAMENTALS OF INDUSTRIAL DATA ANALYTICS3 credits.Provides an understanding of the fundamentals of using data analytics to make data-driven decisions. Emphasizes applying techniques to industrial engineering problems. Focuses on formulating and solving real industrial problems with the appropriate modeling strategies and analytics principles for better decision making.Requisites: (I SY E 210, E C E 331, STAT 311, 324, MATH/​STAT 309, 431, or MATH 531), graduate/professional standing, or member of Engineering Guest Students

ISYE/ME 512 – INSPECTION, QUALITY CONTROL AND RELIABILITY3 credits.Inspection data for quality control; sampling plans for acceptance inspection; charts for process control. Introduction to reliability models and acceptance testing.Requisites: (STAT/​MATH 309, STAT 311, 224, 324, or STAT/​MATH 431), graduate/professional standing, or member of Engineering Guest Students

ISYE 516 — INTRODUCTION TO DECISION ANALYSIS3 credits.Overview of modeling techniques and methods used in decision analysis, including multiattribute utility models, decision trees, and Bayesian models. Psychological components of decision making are discussed. Elicitation techniques for model building are emphasized. Practical applications through real world model building are described and conducted.Requisites: (STAT/​MATH 309, STAT 311, or STAT/​MATH 431), graduate/professional standing, member of Engineering Guest Students, or declared in Capstone Certificate in Artificial Intelligence for Engineering Data Analytics

ISYE/​COMPSCI/​ECE 524 — INTRODUCTION TO OPTIMIZATION3 credits.Introduction to mathematical optimization from a modeling and solution perspective. Formulation of applications as discrete and continuous optimization problems and equilibrium models. Survey and appropriate usage of basic algorithms, data and software tools, including modeling languages and subroutine libraries.Requisites: (COMP SCI 200, 220, 300, 301, 302, 310, or placement into COMP SCI 300) and (MATH 320, 340, 341, or 375) or graduate/professional standing

ISYE 603 — SPECIAL TOPICS IN ENGINEERING ANALYTICS AND OPERATIONS RESEARCH1-3 credits.Various special topics in engineering analytics and operations research, such as machine learning, data management and analysis, optimization, etc.Requisites: None

ISYE 649 — INTERACTIVE DATA ANALYTICS3 credits.A cognitive engineering approach to human-computer interaction and data visualization in particular. Includes a four-part description of effective visualization: design intent, data and application domain, representation and interface features, and human limits and capabilities. The philosophical perspective, scientific basis, and practical tools for effective data visualization and visual analytics. Data processing and how to create static graphs as well as web-based interactive visualizations using the statistical language R.Requisites: I SY E/​PSYCH 349 and (I SY E 210, E C E 331, MATH/​STAT 310, STAT 312, 324, or 340), graduate/professional standing, or member of Engineering Guest Students

ME 459 — COMPUTING CONCEPTS FOR APPLICATIONS IN ENGINEERING3 credits.An overview of computing concepts that support modeling and simulation in engineering applications. Learn the basics of computer architecture, software development and the interplay between software and hardware components.Requisites: COMP SCI 200, 220, 300, 301, 302, 320, or placement into COMP SCI 300, graduate/professional standing, or member of Engineering Guest Students

ECE/COMPSCI/ME 532 – MATRIX METHODS IN MACHINE LEARNING3 credits.Linear algebraic foundations of machine learning featuring real-world applications of matrix methods from classification and clustering to denoising and data analysis. Mathematical topics include: linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include: the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Previous exposure to numerical computing (e.g. Matlab, Python, Julia, R) required.Requisites: (MATH 234, 320, 340, 341, or 375) and (E C E 203, COMP SCI 200, 220, 300, 301, 302, 310, 320, or placement into COMP SCI 300), graduate/professional standing, or declared in Capstone Certificate in Computer Sciences for Professionals

ME 548 — INTRODUCTION TO DESIGN OPTIMIZATION3 credits.Introduces basic concepts and techniques used in the optimization of engineering design components and systems. Pose and solve typical optimization problems such as truss and finite-element-based optimization.Requisites: M E 306 or E M A 303, or graduate/professional standing, or member of Engineering Guest Students

ME/​COMPSCI/​ECE/​EMA/​EP 759 — HIGH PERFORMANCE COMPUTING FOR APPLICATIONS IN ENGINEERING3 credits.An overview of hardware and software solutions that enable the use of advanced computing in tackling computationally intensive Engineering problems. Hands-on learning promoted through programming assignments that leverage emerging hardware architectures and use parallel computing programming languages. Students are strongly encourage to have completed COMP SCI 367 or COMP SCI 400 or to have equivalent experience.Requisites: Graduate/professional standing

EPD 522 — GENERATIVE ARTIFICIAL INTELLIGENCE FOR ENGINEERING APPLICATIONS3 credits.Comprehensive coverage of AI-powered chatbots, from understanding generative AI fundamentals to developing sophisticated chatbot applications. Hands-on experience with generative AI tools. Explore retrieval-augmented generation (RAG) techniques. Examine critical aspects such as security, privacy, and memory models. Knowledge of Python [such as COMP SCI 220 or E P D 455] strongly recommended.Requisites: Graduate/professional standing or declared in Capstone Certificate in Artificial Intelligence for Engineering Data Analytics

ISYE 521 — MACHINE LEARNING IN ACTION FOR INDUSTRIAL ENGINEERS3 credits.Principles, algorithms, and industrial engineering applications of machine learning. Predictive analytics, with a focus on combining data and models to improve decision-making. Methods include: statistics, linear regression, logistic regression, regularization, over-fitting, clustering, classification and regression trees, boosting, bagging, deep learning, and neural networks. Applications areas include: healthcare, transportation, and the public sector.Requisites: (COMP SCI 200, 220, or place into COMP SCI 300),(I SY E 323 or I SY E/​COMP SCI/​E C E 524), and (I SY E 210, STAT 311, 324, STAT/​MATH 309, or 431), grad/prof standing, member of Engr Guest Stdnts, or declared in Capstone Cert in AI for Engr Data Analytics

EPD 611 — ENGINEERING ECONOMICS & MANAGEMENT3 credits.Addresses principles and practices of interpreting financial information and performing engineering-related economic analyses. Focuses on the practical use of economic information for decision-making.Requisites: Graduate/professional standing or declared in Capstone Certificate in Applied Engineering Management

EPD 612 — TECHNICAL PROJECT MANAGEMENT3 credits.Learn key principles and tools of project management applicable to a broad range of engineering projects. Covers techniques for project planning, scheduling, resource allocation, and project tracking, as well as the interface between projects and the organizations within which they are executed.Requisites: Graduate/professional standing

EPD 619 — FOSTERING AND LEADING INNOVATION3 credits.Learn to develop vision, culture, and practices that value and drive innovation within engineering and technical organizations. Grow your ability to build an enterprise that values, pursues, and delivers innovative technical services and products.Requisites: Graduate/professional standing. Not open to students with credit for E P D 708.

ISYE 615 — PRODUCTION SYSTEMS CONTROL3 credits.An intermediate to advanced course stressing the application of recent operations research techniques to production planning, scheduling and inventory control.Requisites: I SY E 315, 320, and 323 and (STAT/​MATH 310, STAT 312 or STAT/​MATH 431), graduate/professional standing, or member of Engineering Guest Students

ISYE 618 — QUALITY ENGINEERING AND QUALITY MANAGEMENT3 credits.Strategic quality planning, change management, problem identification and solving, process improvement, and performance evaluation. Business and decision-making skills related to quality systems and process improvement.Requisites: Graduate/professional standing

ISYE/​ME 641 — DESIGN AND ANALYSIS OF MANUFACTURING SYSTEMS3 credits.Covers a broad range of techniques and tools relevant to the design, analysis, development, implementation, operation and control of modern manufacturing systems. Case studies assignments using industry data will be used to elaborate the practical applications of the theoretical concepts.Requisites: I SY E 315, graduate/professional standing, or member of Engineering Guest Students

EPD 660 — CORE COMPETENCIES OF SUSTAINABILITY3 credits.Introduces real-world pragmatic skills and applications in sustainability competencies. Content reaches across engineering expertise, from chemical engineering to buildings to product design and energy. Modules cover ecological footprinting, lifecycle assessment, resource use and integrated engineering practice.Requisites: Graduate/professional standing

EPD 600 — SPECIAL TOPICS IN ENGINEERING PROFESSIONAL DEVELOPMENT1-3 credits.Topics vary.Requisites: None

OTM 770 — SUSTAINABLE APPROACHES TO SYSTEM IMPROVEMENT4 credits.Innovative system-improvement concepts and approaches that sustainably strengthen mission-central concerns such as quality, cost, customers, markets, revenue, profit, brand, reputation, sourcing, quality of work life, natural capital, buildup of concentrations and base of the pyramid.Requisites: raduate/professional standing or declared in graduate Business Exchange program

EPD 455 — PYTHON FOR APPLICATIONS IN ENGINEERING1 credits.Introduction to Python’s concepts of objects and reference; classes and nested objects. Elements of object-oriented programming in Python. Container types: lists, dictionaries, and tuples. Installing Python packages and managing environments. Scientific computing with Numpy and SciPy. Applications of Python to Data Analysis. Applications of Python to Machine Learning. Applications of Python to embedded systems/robotics.Requisites: Graduate/professional standing

EPD 614 — MARKETING FOR TECHNICAL PROFESSIONALS3 credits.Role and contribution of marketing and product management to overall operations; target marketing and market segmentation; product lifecycle positioning; develop product and marketing plan as part of balanced marketing effort; technical perspective on social, ethical, environmental, and sustainability of marketing and product management decisions..Requisites: Graduate/professional standing. Not open to students declared in Business: Marketing, MBA.

EPD 637 — POLYMER CHARACTERIZATION3 credits.Basic principles used for both quantitative and qualitative characterization of polymeric materials, including both assessment of their synthesis and of their structural features at different length scales. Discussion of techniques such as NMR (Nuclear Magnetic Resonance) and GPC (Gel Permeation Chromatography), thermal characterization, rheological characterization, as well as scattering of various types of electromagnetic radiation. Introduction to characterization methods used in industry and polymer crystallography.Requisites: Graduate/professional standing or declared in Capstone Certificate in Polymer Processing & Manufacturing

EPD 678 — SUPPLY CHAIN MANAGEMENT FOR ENGINEERG3 credits.Examines concepts, management techniques, and current trends in the field of supply chain management with emphasis on topics relevant to engineers. Topics include global logistics, logistics engineering techniques, new product introduction process, purchasing strategy, managing transportation providers, distribution center technology and operations, outsourcing supply chain functions, and an introduction to supply chain information systems.Requisites: Graduate/professional standing

EPD 706 — CHANGE MANAGEMENT1 credits.Provides emerging and practicing professionals foundational knowledge to develop a change management strategy and implement it using proven processes and tools. Become better prepared to deliver effective organizational performance. Applies contemporary concepts and methods in change management through student-selected projects.Requisites: Graduate/professional standing or declared in Capstone Certificate in Foundations of Professional Development

EPD 708 — CREATING BREAKTHROUGH INNOVATIONS1 credits.Explore innovation and how design thinking is a driver of innovation. Learn to use various design thinking methods and tools for analysis and decision-making.Requisites: Graduate/professional standing or declared in Capstone Certificate in Foundations of Professional Development

EPD/GENBUS/MHR 783 — LEADING TEAMS1 credit.Develops knowledge and skills to continuously enhance both individual and team performance and productivity. Provides a foundation for leading teams effectively and improving team dynamics in a variety of organizational settings.Requisites: Graduate/professional standing or declared in graduate Business Exchange program

ME 446 – INTRODUCTION TO FEEDBACK CONTROL3 credits.Overview of linear feedback control analysis and design techniques for mechanical systems. Modeling of linear dynamic mechanical systems (review), derivation of their defining differential equations, and analysis of their response using both transient and frequency response techniques; Analysis and design of feedback control of mechanical systems using classical control transform techniques such as root locus and frequency response; Analysis of system robustness through evaluation of phase and gain margins and the Nyquist stability criterion. Design of feedback controllers for mechanical systems using frequency domain loop-shaping methods. Design domains, including mechanical, thermal, and fluid feedback control systems. Effects of non-ideal system characteristics commonly encountered in mechanical systems, such as compliance, delay, and actuator and sensor saturation. Builds on knowledge of high-level computational programming language such as Matlab or Simulink.Requisites: (M E 340 or E M A 545) and (MATH 319 or 320), graduate/professional standing, member of Engineering Guest Students, or declared in Capstone Certificate in Power Conversion and Control. Not open to students with credit for M E 346.